2026-10-15T09:56:36Z https://data.mendeley.com/oai

oai:data.mendeley.com/cx7j83pr69.1 2026-03-13T11:29:47Z

Ballantyne, John Multi-GPU acceleration of PALABOS fluid solver using C++ standard parallelism Mendeley Data This article presents the principles, software architecture, and performance analysis of the GPU port of the lattice Boltzmann software library Palabos [J. Latt et al., “Palabos: Parallel lattice Boltzmann solver”, Comput. Math. Appl. 81, 334-350, (2021)]. A hybrid CPU-GPU execution model is adopted, in which numerical components are selectively assigned to either the CPU or the GPU, depending on considerations of performance or convenience. This design enables a progressive porting strategy, allowing most features of the original CPU-based codebase to be gradually and seamlessly adapted to GPU execution. The new architecture builds upon two complementary paradigms: a classical object-oriented structure for CPU execution, and a data-oriented counterpart for GPUs, which reproduces the modularity of the original code while eliminating object-oriented overhead detrimental to GPU performance. Central to this approach is the use of modern C++, including standard parallel algorithms and template metaprogramming techniques, which permit the generation of hardware-agnostic computational kernels. This facilitates the development of user-defined, GPU-accelerated components such as collision operators or boundary conditions, while preserving compatibility with the existing codebase and avoiding the need for external libraries or non-standard language extensions. The correctness and performance of the GPU-enabled Palabos are demonstrated through a series of three-dimensional multiphysics benchmarks, including the laminar-turbulent transition in a Taylor-Green vortex, lid-driven cavity flow, and pore-scale flow in Berea sandstone. Despite the high-level abstraction of the implementation, the single-GPU performance is similar to CUDA-native solvers, and multi-GPU tests exhibit good weak and strong scaling across all test cases. Beyond the specific context of Palabos, the porting methodology illustrated here provides a generalizable framework for adapting large, complex C++ simulation codes to GPU architectures, while maintaining extensibility, maintainability, and high computational performance. Computational Physics High Performance Computing Computational Fluid Dynamics Lattice-Boltzmann Method Latt, Jonas Coreixas, Christophe Dataset doi:10.17632/cx7j83pr69.1 oai:data.mendeley.com/cx7j83pr69.1 GNU Affero General Public License https://www.gnu.org/licenses/agpl-3.0.en.html https://data.mendeley.com/datasets/cx7j83pr69 2026-03-13T11:29:47Z